Inhibition of Advanced Glycation End Product Formation by Medicinal Plant Extracts Correlates with Phenolic Metabolites and Antioxidant Activity
Bibliographic record
Abstract
Nonenzymatic formation of advanced glycation end products (AGEs) is accelerated under hyperglycemic conditions characteristic of type 2 diabetes mellitus and contributes to the development of vascular complications. As such, inhibition of AGE formation represents a potential therapeutic target for the prevention and treatment of diabetic complications. In the present study, ethanolic extracts of 17 medicinal plants were assessed for inhibitory effects on in vitro AGE formation through fluorometric and immunochemical detection of fluorescent AGEs and N(ε)-(carboxymethyl)lysine adducts of albumin (CML-BSA), respectively. Most extracts inhibited fluorescent AGE formation with IC (50) values ranging from 0.4 to 38.6 µg/mL and all extracts reduced CML-BSA formation but to differing degrees. Results obtained through both methods were highly correlated. Antiglycation activities were positively correlated with total phenolic content, free radical scavenging activity and reduction in malonyldiadehyde levels following oxidation of low-density lipoprotein, but negatively correlated with lag time to formation of conjugated dienes. Together, these results provide evidence that antioxidant phenolic metabolites mediate the antiglycation activity of our medicinal plant collection, a relationship that likely extends to other medicinal and food plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".